Relationships between body size and percent body fat among Melanesians in Vanuatu.
Bibliographic record
Abstract
Obesity is a global epidemic, and measures to define it must be appropriate for diverse populations for accurate assessment of worldwide risk. Obesity refers to excess body fatness, but is more commonly defined by body mass index (BMI). Body composition varies among populations: Asians have higher percent body fat (%BF), and Pacific Islanders lower %BF at a given BMI compared to Europeans. Many researchers thus propose higher BMI cut-off points for obesity among Pacific Islanders and lower cut-offs for Asians. Because of the great genetic diversity in the Asia-Pacific region, more studies analyzing associations between BMI and %BF among diverse populations remain necessary. We measured height; weight; tricep, subscapular, and suprailiac skinfolds; waist and hip circumference; and %BF by bioelectrical impedance among 546 adult Melanesians from Vanuatu in the South Pacific. We analyzed relationships among anthropometric measurements and compared them to measurements from other populations in the Asia-Pacific region. BMI was a relatively good predictor of %BF among our sample. Based on regression analyses, the BMI value associated with obesity defined by %BF (>25% for men, >35% for women) at age 40 was 27.9 for men and 27.8 for women. This indicates a need for a more nuanced definition of obesity than provided by the common BMI cut-off value of 30. Rather than using population-specific cut-offs for Pacific Islanders, we suggest the World Health Organization's public health action cut-off points (23, 27.5, 32.5, 37.5), which enhance the precision of assessments of population-wide obesity burdens while still allowing for international comparison.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".